As is shown in Table REF , the performance of Mask R-CNN [1]} is far from satisfactory on iShape. We think the drop in performance mainly comes from three drawbacks of the design. Firstly, the feature maps suffer from ambiguity when the IoU is large, which is a common characteristic of crowded scenes of irregular shape objects. Also, Mask R-CNN depends on the proposals of RoI, which may be abandoned by the NMS algorithm due to large IoU and lead to missing of some target objects. Moreover, many thin objects can not be segmented by Mask R-CNN because of its RoI pooling, which resizes the feature maps and lost the view of thin objects. The recent proposed end-to-end object detection approach, DETR [2]}, shake of the reliance of NMS and can better deal with objects with large IoU and achieve better performance, as shown in the table. However, DETR still suffers from the RoI pooling problems and performs badly on thin objects, as shown in Figure REF .
